ML-GSAI/LLaDA

• highreadme#1

  Reposition README opening to clarify project domain

  原因：

  当前

  The README currently starts with links and a "News" section, lacking an immediate, clear statement of purpose.

  复制粘贴的修复

  复制

  Add the following paragraph immediately after the main title `# Large Language Diffusion Models`:
  `ML-GSAI/LLaDA is the official PyTorch implementation for Large Language Diffusion Models, a novel approach to generating high-quality text sequences and building efficient large language models. This repository provides code and models for various LLaDA iterations, including LLaDA-V (vision-language), LLaDA 1.5 (preference alignment), LLaDA-MoE (Mixture-of-Experts), and iLLaDA (improved efficiency).`

  待办完成忽略
• hightopics#2

  Add relevant topics to the repository

  原因：

  复制粘贴的修复

  复制

  ["diffusion-models", "large-language-models", "llm", "pytorch", "generative-ai", "text-generation", "mixture-of-experts", "moe", "vision-language-models"]

  待办完成忽略
• mediumlicense#3

  Add a LICENSE file to the repository

  原因：

  复制粘贴的修复

  复制

  Create a `LICENSE` file in the repository root containing the text of a standard open-source license, such as the MIT License.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How can I use diffusion models to generate high-quality text sequences?

  你：未被推荐
  AI 推荐顺序：

  1. Diffusion-LM
  2. Diffusion-BERT
  3. Masked Diffusion Transformer (MDT)
  4. VQ-VAE + Diffusion
  5. DiffuSeq

  AI 推荐了 5 个替代方案，却始终没点名 ML-GSAI/LLaDA。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are current approaches for building efficient large language models with MoE architecture?

  你：未被推荐
  AI 推荐顺序：

  1. Fairseq
  2. DeepSpeed
  3. Megatron-LM
  4. Hugging Face Transformers Library
  5. JAX
  6. Flax
  7. OpenMoE
  8. Colossal-AI

  AI 推荐了 8 个替代方案，却始终没点名 ML-GSAI/LLaDA。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  fail

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of ML-GSAI/LLaDA?

  pass
  ✓AI 明确点名了 ML-GSAI/LLaDA

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts ML-GSAI/LLaDA in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 ML-GSAI/LLaDA

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo ML-GSAI/LLaDA solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 ML-GSAI/LLaDA

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？